OpenRaiser/Intern-Atlas huggingface dataset:
DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
Venue: arXiv cs.CV 2018
Year: 2018

Recently deep learning has been witnessing widespread adoption in various medical image applications. However, training complex deep neural nets requires large-scale datasets labeled with ground truth, which are often unavailable in many medical image domains. For instance, to train a deep neural net to detect pulmonary nodules in lung computed tomography (CT) images, current practice is to manually label nodule locations and sizes in many CT images to construct a sufficiently large training dataset, which is costly and difficult to scale. On the other hand, electronic medical records (EMR) contain plenty of partial information on the content of each medical image. In this work, we explore how to tap this vast, but currently unexplored data source to improve pulmonary nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework augmented with expectation-maximization (EM), to mine weakly supervised labels in EMRs for pulmonary nodule detection. Experimental results show that DeepEM can lead to 1.5\% and 3.9\% average improvement in free-response receiver operating characteristic (FROC) scores on LUNA16 and Tianchi datasets, respectively, demonstrating the utility of incomplete information in EMRs for improving deep learning algorithms.\footnote{https://github.com/uci-cbcl/DeepEM-for-Weakly-Supervised-Detection.git}
Deep Attentional Structured Representation Learning for Visual Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Structured representations, such as Bags of Words, VLAD and Fisher Vectors, have proven highly effective to tackle complex visual recognition tasks. As such, they have recently been incorporated into deep architectures. However, while effective, the resulting deep structured representation learning strategies typically aggregate local features from the entire image, ignoring the fact that, in complex recognition tasks, some regions provide much more discriminative information than others. In this paper, we introduce an attentional structured representation learning framework that incorporates an image-specific attention mechanism within the aggregation process. Our framework learns to predict jointly the image class label and an attention map in an end-to-end fashion and without any other supervision than the target label. As evidenced by our experiments, this consistently outperforms attention-less structured representation learning and yields state-of-the-art results on standard scene recognition and fine-grained categorization benchmarks.
Structural Operational Semantics for Control Flow Graph Machines
Venue: arXiv cs.PL 2018
Year: 2018

Compilers use control flow graph (CFG) representations of low-level programs because they are suited to program analysis and optimizations. However, formalizing the behavior and metatheory of CFG programs is non-trivial: CFG programs don't compose well, their semantics depends on auxiliary state, and, as a consequence, they do not enjoy a simple equational theory that can be used for reasoning about the correctness of program transformations. Lambda-calculus-based intermediate representations, in contrast, have well-understood operational semantics and metatheory, including rich equational theories, all of which makes them amenable to formal verification. This paper establishes a tight equivalence between (a variant of) Levy's call-by-push-value (CBPV) calculus and a control flow graph machine whose instructions are in static single assignment (SSA) form. The correspondence is made precise via a series of abstract machines that align the transitions of the structural operational semantics of the CBPV language with the computation steps of the SSA form. The target machine, which is derived from the CBPV language, accurately captures the execution model of control flow graphs, including direct jumps, mutually recursive code blocks, and multi-argument function calls, and the closure-free subset is similar to the SSA intermediate representations found in modern compilers such as LLVM and GCC. The definitions of all the language/abstract machine semantics and the theorems relating them are fully verified in Coq.
Overcoming the Challenges of Solar Rover Autonomy: Enabling Long-Duration Planetary Navigation
Venue: arXiv cs.RO 2018
Year: 2018

The successes of previous and current Mars rovers have encouraged space agencies worldwide to pursue additional planetary exploration missions with more ambitious navigation goals. For example, NASA's planned Mars Sample Return mission will be a multi-year undertaking that will require a solar-powered rover to drive over 150 metres per sol for approximately three months. This paper reviews the mobility planning framework used by current rovers and surveys the major challenges involved in continuous long-distance navigation on the Red Planet. It also discusses recent work related to environment-aware and energy-aware navigation, and provides a perspective on how such work may eventually allow a solar-powered rover to achieve autonomous long-distance navigation on Mars.
MIMiS: Minimally Intrusive Mining of Smartphone User Behaviors
Venue: arXiv cs.SI 2018
Year: 2018

How intrusive does a life-saving user-monitoring application really need to be? While most previous research was focused on analyzing mental state of users from social media and smartphones, there is little effort towards protecting user privacy in these analyses. A challenge in analyzing user behaviors is that not only is the data multi-dimensional with a myriad of user activities but these activities occur at varying temporal rates. The overarching question of our work is: Given a set of sensitive user features, what is the minimum amount of information required to group users with similar behavior? Furthermore, does this user behavior correlate with their mental state? Towards answering those questions, our contributions are two fold: we introduce the concept of privacy surfaces that combine sensitive user data at different levels of intrusiveness. As our second contribution, we introduce MIMiS, an unsupervised privacy-aware framework that clusters users in a given privacy surface configuration to homogeneous groups with respect to their temporal signature. In addition, we explore the trade-off between intrusiveness and prediction accuracy. MIMiS employs multi-set decomposition in order to deal with incompatible temporal granularities in user activities. We extensively evaluate MIMiS on real data. Across a variety of privacy surfaces, MIMiS identified groups that are highly homogeneous with respect to self-reported mental health scores. Finally, we conduct an in-depth exploration of the discovered clusters, identifying groups whose behavior is consistent with academic deadlines.
Machine Readable Web APIs with Schema.org Action Annotations
Venue: arXiv cs.IR 2018
Year: 2018

The schema.org initiative led by the four major search engines curates a vocabulary for describing web content. The number of semantic annotations on the web are increasing, mostly due to the industrial incentives provided by those search engines. The annotations are not only consumed by search engines, but also by other automated agents like intelligent personal assistants (IPAs). However, only annotating data is not enough for automated agents to reach their full potential. Web APIs should be also annotated for automating service consumption, so the IPAs can complete tasks like booking a hotel room or buying a ticket for an event on the fly. Although there has been a vast amount of effort in the semantic web services field, the approaches did not gain too much adoption outside of academia, mainly due to lack of concrete incentives and steep learning curves. In this paper, we suggest a lightweight, bottom-up approach based on schema.org actions to annotate Web APIs. We analyse schema.org vocabulary in the scope of lightweight semantic web services literature and propose extensions where necessary. We show that schema.org actions could be a suitable vocabulary for Web API description. We demonstrate our work by annotating existing Web APIs of accommodation service providers. Additionally, we briefly demonstrate how these APIs can be used dynamically, for example, by a dialogue system.
Enumeration of Complex Golay Pairs via Programmatic SAT
Venue: arXiv cs.LO 2018
Year: 2018

We provide a complete enumeration of all complex Golay pairs of length up to 25, verifying that complex Golay pairs do not exist in lengths 23 and 25 but do exist in length 24. This independently verifies work done by F. Fiedler in 2013 that confirms the 2002 conjecture of Craigen, Holzmann, and Kharaghani that complex Golay pairs of length 23 don't exist. Our enumeration method relies on the recently proposed SAT+CAS paradigm of combining computer algebra systems with SAT solvers to take advantage of the advances made in the fields of symbolic computation and satisfiability checking. The enumeration proceeds in two stages: First, we use a fine-tuned computer program and functionality from computer algebra systems to construct a list containing all sequences which could appear as the first sequence in a complex Golay pair (up to equivalence). Second, we use a programmatic SAT solver to construct all sequences (if any) that pair off with the sequences constructed in the first stage to form a complex Golay pair.
Explicit Modelling of Physical Measures: From Event-B to Java
Venue: arXiv cs.SE 2018
Year: 2018

The increasing development of cyber-physical systems (CPSs) requires modellers to represent and reason about physical values. This paper addresses two major, inter-related, aspects that arise when modelling physical measures. Firstly, there is often a heterogeneity of representation; for example: speed can be represented in many different units (mph, kph, mps, etc. . . ). Secondly, there is incoherence in composition; for example: adding a speed to a temperature would provide a meaningless result in the physical world, even though such a purely mathematical operation is meaningful in the abstract. These aspects are problematic when implicit semantics - concerned with measurements - in CPSs are not explicit (enough) in the requirements, design and implementation models. We present an engineering approach for explicitly modelling measurements during all phases of formal system development. We illustrate this by moving from Event-B models to Java implementations, via object oriented design.
Advances in Experience Replay
Venue: arXiv cs.LG 2018
Year: 2018

This project combines recent advances in experience replay techniques, namely, Combined Experience Replay (CER), Prioritized Experience Replay (PER), and Hindsight Experience Replay (HER). We show the results of combinations of these techniques with DDPG and DQN methods. CER always adds the most recent experience to the batch. PER chooses which experiences should be replayed based on how beneficial they will be towards learning. HER learns from failure by substituting the desired goal with the achieved goal and recomputing the reward function. The effectiveness of combinations of these experience replay techniques is tested in a variety of OpenAI gym environments.
Facial Landmark Detection: a Literature Survey
Venue: arXiv cs.CV 2018
Year: 2018

The locations of the fiducial facial landmark points around facial components and facial contour capture the rigid and non-rigid facial deformations due to head movements and facial expressions. They are hence important for various facial analysis tasks. Many facial landmark detection algorithms have been developed to automatically detect those key points over the years, and in this paper, we perform an extensive review of them. We classify the facial landmark detection algorithms into three major categories: holistic methods, Constrained Local Model (CLM) methods, and the regression-based methods. They differ in the ways to utilize the facial appearance and shape information. The holistic methods explicitly build models to represent the global facial appearance and shape information. The CLMs explicitly leverage the global shape model but build the local appearance models. The regression-based methods implicitly capture facial shape and appearance information. For algorithms within each category, we discuss their underlying theories as well as their differences. We also compare their performances on both controlled and in the wild benchmark datasets, under varying facial expressions, head poses, and occlusion. Based on the evaluations, we point out their respective strengths and weaknesses. There is also a separate section to review the latest deep learning-based algorithms. The survey also includes a listing of the benchmark databases and existing software. Finally, we identify future research directions, including combining methods in different categories to leverage their respective strengths to solve landmark detection "in-the-wild".
Visual Comfort Assessment for Stereoscopic Image Retargeting
Venue: arXiv cs.MM 2018
Year: 2018

In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content has aroused extensive attention. However, much less work has been done on the perceptual evaluation of stereoscopic image retargeting. In this paper, we first build a Stereoscopic Image Retargeting Database (SIRD), which contains source images and retargeted images produced by four typical stereoscopic retargeting methods. Then, the subjective experiment is conducted to assess four aspects of visual distortion, i.e. visual comfort, image quality, depth quality and the overall quality. Furthermore, we propose a Visual Comfort Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the characteristics of stereoscopic retargeted images, the proposed model introduces novel features like disparity range, boundary disparity as well as disparity intensity distribution into the assessment model. Experimental results demonstrate that VCA-SIR can achieve high consistency with subjective perception.
Borrowing Safe Pointers from Rust in SPARK
Venue: arXiv cs.PL 2018
Year: 2018

In the field of deductive software verification, programs with pointers present a major challenge due to pointer aliasing. In this paper, we introduce pointers to SPARK, a well-defined subset of the Ada language, intended for formal verification of mission-critical software. Our solution uses a permission-based static alias analysis method inspired by Rust's borrow-checker and affine types, and enforces the Concurrent Read, Exclusive Write policy. This analysis has been implemented in the GNAT Ada compiler and tested against a number of challenging examples. In the paper, we give a formal presentation of the analysis rules for a miniature version of SPARK and prove their soundness. We discuss the implementation and compare our solution with Rust.
Analytical study of 5G NR eMBB co-existence
Venue: arXiv cs.IT 2018
Year: 2018

3GPP release 15 focusing on 5G general outline has been published in December 2017. The major difference with respect to currently deployed LTE is the support of various physical layer numerologies. Making the physical layer scalable allows to properly address new services such as low latency or millimeter communications. However it poses the problem of numerology coexistence. Indeed the orthogonality of the OFDM waveform is broken by the use of different subcarrier spacings and therefore multiplexed communications may interfere with each others. A first and simple solution to limit the distortion is to consider guard bands. In this paper, the authors develop analytical metrics to quantify the level of distortion induced by 5G multi-service multiplexing. Besides, general comments and guard band dimensioning are carried out. It is shown that high interference rejection needs to be associated with side lobe reduction techniques to favor an efficient bandwidth use.
Image Co-segmentation via Multi-scale Local Shape Transfer
Venue: arXiv cs.CV 2018
Year: 2018

Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category. In real-world cases, however, common foreground objects often vary greatly in appearance, making their global shapes highly inconsistent across images and difficult to be segmented. To address this problem, this paper proposes a novel co-segmentation approach that transfers patch-level local object shapes which appear more consistent across different images. In our framework, a multi-scale patch neighbourhood system is first generated using proposal flow on arbitrary image-pair, which is further refined by Locally Linear Embedding. Based on the patch relationships, we propose an efficient algorithm to jointly segment the objects in each image while transferring their local shapes across different images. Extensive experiments demonstrate that the proposed method can robustly and effectively segment common objects from an image set. On iCoseg, MSRC and Coseg-Rep dataset, the proposed approach performs comparable or better than the state-of-thearts, while on a more challenging benchmark Fashionista dataset, our method achieves significant improvements.
The Essential Guide to Realizing 5G-Connected UAVs with Massive MIMO
Venue: arXiv cs.IT 2018
Year: 2018

What will it take for drones -- and the whole associated ecosystem -- to take off? Arguably, infallible command and control (C&C) channels for safe and autonomous flying, and high-throughput links for multi-purpose live video streaming. And indeed, meeting these aspirations may entail a full cellular support, provided through 5G-and-beyond hardware and software upgrades by both mobile operators and manufacturers of these unmanned aerial vehicles (UAVs). In this article, we vouch for massive MIMO as the key building block to realize 5G-connected UAVs. Through the sheer evidence of 3GPP-compliant simulations, we demonstrate how massive MIMO can be enhanced by complementary network-based and UAV-based solutions, resulting in consistent UAV C&C support, large UAV uplink data rates, and harmonious coexistence with legacy ground users.
Building an Ecosystem for the Tyrolean Tourism Knowledge Graph
Venue: arXiv cs.IR 2018
Year: 2018

The introduction of the schema.org vocabulary was a big step towards making websites machine read- and understandable. Due to schema.org's RDF-like nature storing annotations in a graph database is easy and efficient. In this paper the authors show how they gather touristic data in the Austrian region of Tirol and provide this data publicly in a knowledge graph. The definition of subsets of the vocabulary is followed by providing means to map data sources efficiently to schema.org and then store the annotated content into the graph. To showcase the consumption of the touristic data four scenarios are described which use the knowledge graph for real life applications and data analysis.
Leveraging human knowledge in tabular reinforcement learning: A study of human subjects
Venue: arXiv cs.AI 2018
Year: 2018

Reinforcement Learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort and expertise on the human designer's part. To date, human factors are generally not considered in the development and evaluation of possible RL approaches. In this article, we set out to investigate how different methods for injecting human knowledge are applied, in practice, by human designers of varying levels of knowledge and skill. We perform the first empirical evaluation of several methods, including a newly proposed method named SASS which is based on the notion of similarities in the agent's state-action space. Through this human study, consisting of 51 human participants, we shed new light on the human factors that play a key role in RL. We find that the classical reward shaping technique seems to be the most natural method for most designers, both expert and non-expert, to speed up RL. However, we further find that our proposed method SASS can be effectively and efficiently combined with reward shaping, and provides a beneficial alternative to using only a single speedup method with minimal human designer effort overhead.
Optimal Finite Length Coding Rate of Random Linear Network Coding Schemes
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we propose a methodology to compute the optimal finite-length coding rate for random linear network coding schemes over a line network. To do so, we first model the encoding, reencoding, and decoding process of different coding schemes in matrix notation and corresponding error probabilities. Specifically, we model the finite-length performance for random linear capacity-achieving schemes: non-systematic (RLNC) and systematic (SNC) and non-capacity achieving schemes: SNC with packet scheduling (SNC-S) or sliding window (SWNC). Then, we propose a binary searching algorithm to identify optimal coding rate for given target packet loss rate. We use our proposed method to obtain the region of exponential increase of optimal coding rate and corresponding slopes for representative types of traffic and erasure rates. Our results show the tradeoff for capacity-achieving codes vs non-capacity achieving schemes, since the latter trade throughput with delay, which is reflected in the decrease of the exponential slope with the blocklength. We also show the effect of the number of re-encoding times, which further decreases the slope.
Parallel Working-Set Search Structures
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we present two versions of a parallel working-set map on p processors that supports searches, insertions and deletions. In both versions, the total work of all operations when the map has size at least p is bounded by the working-set bound, i.e., the cost of an item depends on how recently it was accessed (for some linearization): accessing an item in the map with recency r takes O(1+log r) work. In the simpler version each map operation has O((log p)^2+log n) span (where n is the maximum size of the map). In the pipelined version each map operation on an item with recency r has O((log p)^2+log r) span. (Operations in parallel may have overlapping span; span is additive only for operations in sequence.) Both data structures are designed to be used by a dynamic multithreading parallel program that at each step executes a unit-time instruction or makes a data structure call. To achieve the stated bounds, the pipelined data structure requires a weak-priority scheduler, which supports a limited form of 2-level prioritization. At the end we explain how the results translate to practical implementations using work-stealing schedulers. To the best of our knowledge, this is the first parallel implementation of a self-adjusting search structure where the cost of an operation adapts to the access sequence. A corollary of the working-set bound is that it achieves work static optimality: the total work is bounded by the access costs in an optimal static search tree.
Alien wavelength technique to enhance garr optical network
Venue: arXiv cs.NI 2018
Year: 2018

GARR optical network used to be composed of two separate optical network domains on its national infrastructure. With the aim to integrate these two domains and deliver high performance services all over its infrastructure, we implemented the so called alien wavelength technique, thus improving the overall efficiency of the Italian research and education network in a cost-effective way. This paper describes the activity, results, and our experience in the integration of alien wavelengths in a production environment, with a special emphasis on deployment and operational issues.